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Opiniones y comentarios de aprendices correspondientes a Introduction to Deep Learning por parte de HSE University

4.5
estrellas
1,830 calificaciones
428 reseña

Acerca del Curso

The goal of this online course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image. The prerequisites for this course are: 1) Basic knowledge of Python. 2) Basic linear algebra and probability. Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand: 1) Linear regression: mean squared error, analytical solution. 2) Logistic regression: model, cross-entropy loss, class probability estimation. 3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions. 4) The problem of overfitting. 5) Regularization for linear models. Do you have technical problems? Write to us: coursera@hse.ru....

Principales reseñas

DK
19 de sep. de 2019

one of the excellent courses in deep learning. As stated its advanced and enjoyed a lot in solving the assignments. looking forward for more such courses especially in Natural language processing

TP
8 de ago. de 2020

A very good course and it is truly insightful. This course deals with more on the concepts therefore I have a better understanding of what is really happening when I build deep learning models.

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176 - 200 de 426 revisiones para Introduction to Deep Learning

por Ravindran M

4 de dic. de 2018

Good course. More detailed lectures may have been helpful.

por Subham T 1

21 de feb. de 2018

Nice course with all the concepts explained in a lucid way

por Udith D B H

12 de mar. de 2020

Excellent course which expects much practical knowledge.

por MEET S

18 de abr. de 2020

Really useful to learn the basics of Deep Learning.

por Debasis U

3 de jul. de 2019

I surely have learned a lot from the course, thanks

por Mohammed S E

19 de abr. de 2019

very detailed , clear and to the point , i loved it

por Marcos S

26 de jun. de 2021

t​otally recommended! robust and clear foundations

por Samuel R

12 de ago. de 2019

Great lectures and homeworks.They were challenging

por zhaoyulai

19 de jul. de 2018

课程不错, 但英语听不懂, Inception Fine-tune难度比较大, 运行耗时间且电脑发热

por Eyvaz N

8 de mar. de 2021

The best intro to Deep Learning I have ever seen

por Vaibhav O

17 de mar. de 2019

Excellent hands on exercises to learn the basics

por Xin Q

8 de oct. de 2019

A really nice course as an introduction to DL.

por Tom M

11 de may. de 2018

Fun course that teaches some very cool topics.

por Yu C

11 de jul. de 2020

Clearly presented with desirable formal rigor

por Faris G

8 de jun. de 2018

Really inspired me to continue deep learning.

por Aditya N J

27 de sep. de 2020

Awesome course . A must recommended for all

por Bharath R R

20 de may. de 2020

Great Course with good programing Excercise

por RLee

26 de mar. de 2019

A very comprehensive "introduction" course!

por Firas B

26 de nov. de 2018

One of the best courses of deep learning !!

por Arturo O A

22 de ago. de 2018

Really great course, I highly recommend it!

por Biswa s

7 de ene. de 2018

Good Introduction. The assignments are good

por Jayesh G

24 de jul. de 2019

Nice course....refreshed a lot of concepts

por Daniel M

30 de abr. de 2018

Challenging, educational, and tons of fun.

por Лупашин Е И

2 de abr. de 2021

топ курс спасибо любимой ВШЭ за него!!!

por sanghoon.lee1@kbfg.com

27 de oct. de 2020

I like this lecture. thank you so much.